Principal component analysis (PCA) is a popular technique that is useful for dimensionality reduction but it is affected by the presence of outliers. The outlier sensitivity of classical PCA (CPCA) has caused the development of new approaches. Effects of using estimates obtained by expectation-maximization -EM and multiple imputation -MI instead of outliers were examined on the artificial and a real data set. Furthermore, robust PCA based on minimum covariance determinant (MCD), PCA based on estimates obtained by EM instead of outliers and PCA based on estimates obtained by MI instead of outliers were compared with the results of CPCA. In this study, we tried to show the effects of using estimates obtained by MI and EM instead of outliers, depending on the ratio of outliers in data set. Finally, when the ratio of outliers exceeds 20%, we suggest the use of estimates obtained by MI and EM instead of outliers as an alternative approach.
Optimal planning of the health workers is of vital importance for a country. Distribution of health workers among provinces in emerging markets is an important development criterion. In this study, biplot graphical approach is used to determine the distribution of health workers. The results of biplot analysis point out that the distribution of the healthcare staff in Turkey is unbalanced. The number of health workers should be planned and considered according to the desire, need, population, target and workload criteria. The new employment opportunities should be created and the workers should be encouraged to serve in low income regions by providing better conditions.
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